Systems and methods for intelligently-tuned digital self-interference cancellation

ABSTRACT

A system for digital self-interference cancellation includes a filter that generates a reduced-noise digital residue signal; a channel estimator that generates a current self-interference channel estimate from a digital transmit signal, the reduced-noise digital residue signal, and past self-interference channel estimates; a controller that dynamically sets the digital transform configuration in response to changes in a controller-sampled digital residue signal; a predictor that modifies output of the channel estimator to compensate for a first time delay incurred in tuning the system for digital self-interference cancellation; and a channel memory that stores the past self-interference channel estimates.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/450,092, filed 24 Jun. 2019, which is a continuation of U.S. patentapplication Ser. No. 16/125,269, filed on 7 Sep. 2018, now issued asU.S. Pat. No. 10,382,089, which is a continuation of U.S. patentapplication Ser. No. 15/937,605, filed on 27 Mar. 2018, now issued asU.S. Pat. No. 10,103,774, which claims the benefit of U.S. ProvisionalApplication Ser. No. 62/477,301, filed on 27 Mar. 2017, all of which areincorporated in their entireties by this reference.

TECHNICAL FIELD

This invention relates generally to the wireless communications field,and more specifically to new and useful systems and methods forintelligently-tuned digital self-interference cancellation.

BACKGROUND

Traditional wireless communication systems are half-duplex; that is,they are not capable of transmitting and receiving signalssimultaneously on a single wireless communications channel. Recent workin the wireless communications field has led to advancements indeveloping full-duplex wireless communications systems; these systems,if implemented successfully, could provide enormous benefit to thewireless communications field. For example, the use of full-duplexcommunications by cellular networks could cut spectrum needs in half.One major roadblock to successful implementation of full-duplexcommunications is the problem of self-interference. While progress hasbeen made in this area, many of the solutions intended to addressself-interference fall short in performance, especially when tuningdigital self-interference cancellation systems. Thus, there is a need inthe wireless communications field to create new and useful systems andmethods for intelligently-tuned digital self-interference cancellation.This invention provides such new and useful systems and methods.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic representation of a full-duplex transceiver;

FIG. 2 is a schematic representation of a system of an inventionembodiment;

FIG. 3A is a schematic representation of a digital self-interferencecanceller of a system of an invention embodiment;

FIG. 3B is a schematic representation of a digital self-interferencecanceller of a system of an invention embodiment;

FIG. 3C is a schematic representation of a digital self-interferencecanceller of a system of an invention embodiment;

FIG. 4 is a schematic representation of a digital self-interferencecanceller of a system of an invention embodiment;

FIG. 5 is a schematic representation of a digital self-interferencecanceller of a system of an invention embodiment;

FIG. 6 is a schematic representation of a digital self-interferencecanceller of a system of an invention embodiment;

FIG. 7 is a schematic representation of a digital self-interferencecanceller of a system of an invention embodiment;

FIG. 8 is a schematic representation of a digital self-interferencecanceller of a system of an invention embodiment;

FIG. 9A is a plot representation of a self-interference channelestimate;

FIG. 9B is a plot representation of a predicted self-interferencechannel estimate;

FIG. 10 is a schematic representation of a predictor of a digitalself-interference canceller of a system of an invention embodiment;

FIG. 11 is a schematic representation of a predictor of a digitalself-interference canceller of a system of an invention embodiment;

FIG. 12 is a schematic representation of a predictor of a digitalself-interference canceller of a system of an invention embodiment;

FIG. 13 is a schematic representation of a digital self-interferencecanceller of a system of an invention embodiment;

FIG. 14 is a schematic representation of a digital self-interferencecanceller of a system of an invention embodiment;

FIG. 15 is a schematic representation of a digital self-interferencecanceller of a system of an invention embodiment;

FIG. 16A is a plot representation of a predicted self-interferencechannel estimate;

FIG. 16B is a plot representation of a time-interpolated predictedself-interference channel estimate;

FIG. 17 is a plot representation of self-interference channel magnitudesmoothing; and

FIG. 18 is a plot representation of self-interference channel phasesmoothing.

DESCRIPTION OF THE INVENTION EMBODIMENTS

The following description of the invention embodiments of the inventionis not intended to limit the invention to these invention embodiments,but rather to enable any person skilled in the art to make and use thisinvention.

1. Full-duplex Wireless Communication Systems

Wireless communications systems have revolutionized the way the worldcommunicates, and the rapid growth of communication using such systemshas provided increased economic and educational opportunity across allregions and industries. Unfortunately, the wireless spectrum requiredfor communication is a finite resource, and the rapid growth in wirelesscommunications has also made the availability of this resource everscarcer. As a result, spectral efficiency has become increasinglyimportant to wireless communications systems.

One promising solution for increasing spectral efficiency is found infull-duplex wireless communications systems; that is, wirelesscommunications systems that are able to transmit and receive wirelesssignals at the same time on the same wireless channel. This technologyallows for a doubling of spectral efficiency compared to standardhalf-duplex wireless communications systems.

While full-duplex wireless communications systems have substantial valueto the wireless communications field, such systems have been known toface challenges due to self-interference; because reception andtransmission occur at the same time on the same channel, the receivedsignal at a full-duplex transceiver may include undesired signalcomponents from the signal being transmitted from that transceiver. As aresult, full-duplex wireless communications systems often include analogand/or digital self-interference cancellation circuits to reduceself-interference.

Full-duplex transceivers preferably sample transmission output asbaseband digital signals, intermediate frequency (IF) analog signals, oras radio-frequency (RF) analog signals, but full-duplex transceivers mayadditionally or alternatively sample transmission output in any suitablemanner. This sampled transmission output may be used by full-duplextransceivers to remove interference from received wirelesscommunications data (e.g., as RF/IF analog signals or baseband digitalsignals). In many full-duplex transceivers, an analog self-interferencecancellation system is paired with a digital self-interferencecancellation system. The analog cancellation system removes a firstportion of self-interference by summing delayed and scaled versions ofthe RF transmit signal to create an RF self-interference cancellationsignal, which is then subtracted from the RF receive signal.Alternatively, the analog cancellation system may perform similar tasksat an intermediate frequency. After the RF (or IF) receive signal hasthe RF/IF self-interference cancellation signal subtracted, it passesthrough an analog-to-digital converter of the receiver (and becomes adigital receive signal). After this stage, a digital self-interferencecancellation signal (created by transforming a digital transmit signal)is then subtracted from the digital receive signal.

Full-duplex transceivers often include tuning systems that adjusttunable parameters of the analog self-interference cancellation systemin order to adapt the analog self-interference cancellation signal tochanging self-interference conditions. Likewise, full-duplextransceivers may similarly include tuning systems that alter thetransform configuration of digital self-interference cancellationsystems for the same purpose.

Well-tuned digital and analog self-interference cancellation systems aregenerally effective for reducing interference, but tuning in thesesystems is often a time-consuming process. This poses a problem: thelonger a system takes to retune, the more likely it is that the systemwill be unable to adapt to rapidly changing self-interferencecharacteristics. Consequently, the usefulness of full-duplextransceivers may be limited.

The systems and methods described herein increase tuning performance offull-duplex transceivers as shown in FIG. 1 (and other applicablesystems) by performing digital self-interference canceller tuning, thusallowing for increased effectiveness in self-interference cancellation.Other applicable systems include active sensing systems (e.g., RADAR),wired communications systems, wireless communications systems, and/orany other suitable system, including communications systems wheretransmit and receive bands are close in frequency, but not overlapping.

2. System for Intelligently-tuned Digital Self-interference Cancellation

As shown in FIG. 2, a system 100 for intelligently-tuned digitalself-interference cancellation includes a digital self-interferencecanceller 140. The system 100 may additionally or alternatively includea receiver 110, a transmitter 120, a signal coupler 130,analog-to-digital converters (ADCs) 150 and 151, a digital-to-analogconverter (DAC) 152, and an analog canceller 160.

The system 100 functions to increase the performance ofself-interference cancellation by performing digital self-interferencecanceller tuning intelligently based on both transmit signal input andresidue signal input. Transmit signal input is used to identifycomponents of a transmit signal likely to be reflected in receivedself-interference, while residue signal input is used to determine theeffects of self-interference cancellation.

The system 100 is preferably implemented using both digital and analogcircuitry. Digital circuitry is preferably implemented using ageneral-purpose processor, a digital signal processor, an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA) and/or any suitable processor(s) or circuit(s). Analog circuitryis preferably implemented using analog integrated circuits (ICs) but mayadditionally or alternatively be implemented using discrete components(e.g., capacitors, resistors, transistors), wires, transmission lines,waveguides, digital components, mixed-signal components, or any othersuitable components. The system 100 preferably includes memory to storeconfiguration data, but may additionally or alternatively be configuredusing externally stored configuration data or in any suitable manner.

The receiver 110 functions to receive analog receive signals transmittedover a communications link (e.g., a wireless channel, a coaxial cable).The receiver no preferably converts analog receive signals into digitalreceive signals for processing by a communications system, but mayadditionally or alternatively not convert analog receive signals(passing them through directly without conversion).

The receiver 110 is preferably a radio-frequency (RF) receiversubstantially similar to the receiver of U.S. patent application Ser.No. 15/362,289, the entirety of which is incorporated by this reference,but may additionally or alternatively be any suitable receiver.

The transmitter 120 functions to transmit signals of the communicationssystem over a communications link to a second communications system. Thetransmitter 120 preferably converts digital transmit signals into analogtransmit signals.

The transmitter 120 is preferably a radio-frequency (RF) transmittersubstantially similar to the transmitter of U.S. patent application Ser.No. 15/362,289, but may additionally or alternatively be any suitabletransmitter.

The signal coupler 130 functions to allow signals to be split and/orjoined. The signal coupler 130 may be used to provide a sample of theanalog transmit signal for the digital canceller 140 and/or analogcancellers 160; that is, the signal coupler 130 may serve as a transmitcoupler. The signal coupler 130 may also be used to combine one or moreanalog self-interference cancellation signals (from analog/digitalcancellers) with the analog receive signal; that is, the signal coupler130 may serve as a receive coupler. Additionally or alternatively, thesignal coupler 130 may be used for any other purpose. For example, asshown in FIG. 2, a signal coupler 130 may be used to provide a sample ofa residue signal (in this case, an analog receive signal that hasalready been combined with an analog self-interference cancellationsignal) to the digital canceller 140. The signal coupler 130 ispreferably substantially similar to the signal coupler of U.S. patentapplication Ser. No. 15/362,289, but may additionally or alternativelybe any suitable signal coupler.

The digital self-interference canceller 140 functions to produce adigital self-interference cancellation signal from a digital transmitsignal, as shown in FIG. 3A, FIG. 3B, and FIG. 3C. The digitalself-interference cancellation signal is preferably converted to ananalog self-interference cancellation signal (by the DAC 152) andcombined with one or more analog self-interference cancellation signalsto further reduce self-interference present in the RF receive signal atthe receiver 110. Additionally or alternatively, the digitalself-interference cancellation signal may be combined with a digitalreceive signal (e.g., after the receiver no, as shown in FIG. 1).

The digital self-interference canceller 140 preferably samples the RFtransmit signal of the transmitter 120 using the ADC 150 (additionallyor alternatively, the canceller 140 may sample the digital transmitsignal or any other suitable transmit signal) and transforms the sampledand converted RF (or IF) transmit signal to a digital self-interferencesignal based on a digital transform configuration. The digital transformconfiguration preferably includes settings that dictate how the digitalself-interference canceller 140 transforms the digital transmit signalto a digital self-interference signal (e.g. coefficients of ageneralized memory polynomial used to transform the transmit signal to aself-interference signal).

Note that the digital self-interference canceller 140 may be coupled toany transmit and/or receive signals (either as input to the canceller oras outputs of the canceller), as described in U.S. patent applicationSer. No. 14/569,354, the entirety of which is incorporated by thisreference. For example, the digital self-interference canceller may takeas input an RF-sourced intermediate frequency (IF) transmit signal(e.g., the transmit signal is converted to RF by the transmitter 120,then downconverted to IF by a downcoverter, then passed through the ADC150 to the digital canceller 140) or may output at IF (e.g., the digitalself-interference cancellation signal is converted to adigitally-sourced IF self-interference cancellation signal, and is thencombined with an IF self-interference cancellation signal at IF, beforethe combined self-interference cancellation signal is converted to RFand combined with an RF receive signal).

The digital self-interference canceller 140 may be implemented using ageneral-purpose processor, a digital signal processor, an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA) and/or any suitable processor(s) or circuit(s). The digitalself-interference canceller 140 preferably includes memory to storeconfiguration data, but may additionally or alternatively be configuredusing externally stored configuration data or in any suitable manner.

The digital self-interference canceller 140 preferably includes a filter142, a channel estimator 143, a controller 144, and channel memory 145,as shown in FIG. 3A, FIG. 3B, and FIG. 3C. The digital self-interferencecanceller may additionally or alternatively include a transformer 141, apredictor 146, and/or an extender 147.

The transformer 141 functions to transform signals (e.g., a digitaltransmit signal, an output signal of the canceller 140) into analternate-basis representation to perform some function (generally, tocalculate the self-interference channel before transforming the channelback into the original representation).

For example, in one implementation as shown in FIG. 4, transformers 141may be used to convert time-domain input signals into the frequencydomain (via a Fourier transform). The Fourier-transformed input is usedto generate a self-interference cancellation signal, which is thenconverted back to the time domain by another transformer 141. In thisimplementation, the Fourier transform may be performed using a DiscreteFourier Transform (DFT) matrix. Likewise, the inverse Fourier transformmay be calculated using the pseudo-inverse of the DFT matrix; i.e., anInverse Discrete Fourier Transform (IDFT) matrix. Calculating the IDFTusing the DFT without modification results in the same number of timedomain signal components generated (e.g., ‘taps’ of theself-interference cancellation channel) as frequency points in theoriginal DFT.

In many cases, it may be desirable to perform the DFT using a highresolution matrix (i.e., use many frequencies), while receiving alower-resolution (i.e., fewer number of time domain components)solution. Likewise, it may be desirable to restrict the solution tospecific frequencies of interest (or simply lower frequency resolutionused to generate the solution). To accomplish this, in one embodiment, atransformer 141 may perform the IFFT using a reduced IDFT matrixcalculated by first reducing the number of time and frequency domaincomponents (e.g., zeroing out parts of the matrix, neglecting parts ofthe matrix) present in the DFT matrix and then calculating thepseudo-inverse of this reduced DFT matrix to generate the reduced IDFTmatrix.

Frequency and time domain components may be removed in any manner and toaccomplish any goal. For example, time domain components may be removedto match a certain solution size (e.g., 64 taps), and frequencycomponents may be removed if they are not considered frequencies ofparticular interest (e.g., as determined from a lookup table of whichreceive frequencies are important, by measuring the accuracy ofself-interference cancellation at different frequencies, by measuringthe amount of self-interference present at different signals, etc.).Alternatively, DFT matrix reduction may be performed in any way.

While the transformer 141 may perform Fourier (or inverse Fourier)transforms as a basis-changing technique, the transformer 141 mayadditionally or alternatively perform any suitable basis-changingtransformation. For example, many components of the self-interferencechannel solution may be correlated; this correlation can result in slowconvergence of transform configuration parameters. Resultantly, in oneimplementation of a preferred embodiment, the transformers 141 mayorthogonalize (or otherwise decorrelate) input components using adecorrelation matrix. For example, this decorrelation may be performedusing a static decorrelation matrix based on a Gaussian inputdistribution (or other expected signal distribution) for a giventransmission power or using a static decorrelation matrix based onsubtraction of expected transmission power; e.g.,

$\begin{bmatrix} \\ \\

\end{bmatrix} = {\begin{bmatrix}1 & 0 & 0 \\{{- 3}\sigma^{2}} & 1 & 0 \\\left( {15\sigma^{4}} \right) & {{- 10}\sigma^{2}} & 1\end{bmatrix}*\begin{bmatrix}s_{1} \\{{s_{1}}^{2}*s_{1}} \\{{s_{1}}^{4}*s_{1}}\end{bmatrix}}$where σ² is the power of the linear transmit signal s₁.

Transformers 141 may alternatively perform decorrelation using any othermethod, including using singular value decomposition (SVD), adynamically computed decorrelation matrix, and/or a matrix similar tothe above exemplified static matrix, except with expected power computeddynamically.

The filter 142 functions to reduce noise in the signals received at thefilter. For example, the filter 142 may receive a transmit signal Tx anda residue signal Rxr (the receive signal Rx after digitalself-interference cancellation), as shown in FIG. 3A. Additionally oralternatively, the filter 142 may receive any signals relevant tointerference cancellation (e.g., Rx instead of Rxr, or a ratio ofsignals; e.g., Rxr/Tx). Note that the filter 142 may additionally oralternatively pass signals without filtering. For example, the filter142 may pass a digital transmit signal without filtering it. Further, ifthe filter 142 outputs a signal based on the same content to differentdestinations, the filter 142 may filter the signal differently based onthe destination. For example, the filter 142 may output a first filtereddigital residue signal to the controller 144 and a second filtereddigital residue signal to the channel estimator 143, and the parametersof filtering (and thus the output signals themselves) may benon-identical between the first and second filtered digital residuesignals.

Rx may be written as follows: Rx=TxH+Z, where Rx is the receive signal(noting that this receive signal may already have some amount ofinterference cancellation resulting from analog cancellation), Tx is thetransmit signal, H is the self-interference channel, and Z is noise(which includes an actual receive signal if present). Likewise, theresidue signal after digital self-interference cancellation may bewritten as Rxr=Tx(H−Ĥ)+Z, where Ĥ is a self-interference channelestimate and −TxĤ represents the self-interference cancellation signal.

The filter 142 preferably reduces input noise by performingtime-averaging of input signals to prepare the signals forself-interference cancellation signal generation. The filter 142 mayperform time-averaging in any manner; e.g., block averaging, movingaveraging, infinite impulse response (IIR) filtering, etc.

Time averaging functions to reduce the effect of noise in channelestimates (e.g., as Z varies independent of H). As discussed in latersections, the controller 144 preferably dynamically adjusts the numberof samples the filter 142 uses to perform averaging (i.e., the averagingwindow) to improve canceller 140 performance. Larger sampling windowsallow for increased immunity to noise, but at the cost of ability totrack rapid self-interference channel variation. The controller 144 mayadditionally or alternatively vary any aspect of filtering.

The filter 142 may additionally or alternatively perform any signaltransformation to aid in preparing input signals for self-interferencecancellation. For example, the filter 142 may perform sample rateconversion of signals, scaling, shifting, and/or otherwise modifyingsignals.

In one implementation, the filter 142 modifies sampled digital transmitsignals by removing information unlikely to substantially affect theoutput of the channel estimator 143. This may include, for instance,dropping samples if the samples do not represent a change above somechange threshold from previous samples. As another example, if digitaltransmit signals correspond to a particular amplitude of an outputanalog signal, only digital signal data corresponding to an amplitudeabove some amplitude threshold may be passed to the channel estimator143.

If the filter 142 receives digital transmit signals from more than onesource (e.g. from both the digital transmit line before the RFtransmitter and the analog transmit line after the RF transmitter via anADC), the filter 142 may additionally or alternatively combine thesignals in any suitable way or may select one signal over another. Forinstance, the filter 142 may pass the average of the two signals to theestimator 143. As another example, the filter 142 may prefer anRF-sourced digital transmit signal (e.g., from the ADC 150) over thetransmit-path digital transmit signal (e.g., sampled before conversionby the transmitter 120) above a certain transmitter power, and viceversa at or below that transmitter power. The selection and combinationof the two (or more) signals may be dependent on any suitable condition.

The filter 142 preferably passes both the digital transmit signal andthe digital residue (i.e., the digital receive signal after the digitalreceive signal has been combined with the digital self-interferencecancellation signal output by the system 100) but may additionally oralternatively pass any signals (e.g., a combination of transmit andresidue, receive signal prior to combination with self-interferencecancellation signal, etc.). The digital transmit signal after filteringmay be referred to as a reduced-noise digital transmit signal; likewise,if the residue is filtered, it may be referred to as a reduced-noiseresidue signal.

The channel estimator 143 functions to generate a currentself-interference cancellation channel estimate (Ĥ) from the output ofthe filter 142 or from any other suitable signal source.

The channel estimator 143 preferably generates a channel estimate from aweighted sum of signal components according to mathematical modelsadapted to model self-interference contributions of the RF transmitter,RF receiver, and/or other sources. Examples of mathematical models thatmay be used by the channel estimator 143 include generalized memorypolynomial (GMP) models, Volterra models, and Wiener-Hammerstein models;the channel estimator 143 may additionally or alternatively use anycombination or set of models.

The channel estimator 143 may additionally or alternatively usegenerated mathematical models for modeling self-interferencecontributions based on comparisons of sampled digital transmit signalsto received signals (from the receive path or any other suitablesource). These models may be generated from previously known models ormay be created using neural network and/or machine learning techniques.

The channel estimator 143 preferably performs channel estimategeneration according to a transform configuration set dynamically by thecontroller 144 (discussed in more detail in sections covering thecontroller 144). Additionally or alternatively, the channel estimator143 may combine signal components in any manner in order to generate aself-interference channel estimate.

The channel estimator 143 preferably generates the self-interferencechannel estimate from a ratio of residue and transmit signals (e.g.,Rxr/Tx) but may additionally or alternatively generate self-interferencechannel estimates from any signal data.

In addition to generating a self-interference channel estimate, thechannel estimator 143 may also generate a self-interference cancellationsignal by combining a digital transmit signal and the self-interferencechannel estimate (e.g., as shown in FIG. 3B). As a first alternative,the channel estimator 143 may pass the self-interference channelestimate along with the transmit signal without combining the two (e.g.,as shown in FIG. 3A). As a second alternative, the channel estimator 143may pass the self-interference channel estimate without passing thetransmit signal. As a third alternative, the channel estimator 143 maypass any signal data relevant to self-interference cancellation.

The digital self-interference canceller 140 preferably includes a singlechannel estimator 143, but may additionally or alternatively includemultiple channel estimators 143. For example, the digitalself-interference canceller 140 may include one channel estimator 143for linear self-interference cancellation and one for non-linearself-interference cancellation, as shown in FIG. 5. Signal componentsmay be transmitted to multiple channel estimators 143 in any manner. Ifthe canceller 140 includes multiple channel estimators 143, the outputof these filters may be combined in any manner to generate aself-interference cancellation signal.

The controller 144 functions to set the transform configuration of thechannel estimator 143. The controller 144 may additionally oralternatively set configuration of the filter 142.

The transform configuration preferably includes the type of model ormodels used by the channel estimator 143 as well as configurationdetails pertaining to the models (each individual model is a model typepaired with a particular set of configuration details). For example, onetransform configuration might set the channel estimator 143 to use a GMPmodel with a particular set of coefficients. If the model type isstatic, the transform configuration may simply include modelconfiguration details; for example, if the model is always a GMP model,the transform configuration may include only coefficients for the model,and not data designating the model type.

The transform configuration may additionally or alternatively includeother configuration details related to the filter 142 and/or the channelestimator 143. For example, if the channel estimator 143 includesmultiple transform paths, the controller 144 may set the number of thesetransform paths, which model order their respective component generatorscorrespond to, and/or any other suitable details. In general, thetransform configuration may include any details relating to thecomputation or structure of the filter 142 and/or the channel estimator143.

Transform configurations are preferably selected and/or generated by thecontroller 144. The controller 144 may set an appropriate transformconfiguration by selecting from stored static configurations, fromgenerating configurations dynamically, or by any other suitable manneror combination of manners. For example, the controller 144 may choosefrom three static transform configurations based on their applicabilityto particular signal and/or environmental conditions (the first isappropriate for low transmitter power, the second for medium transmitterpower, and the third for high transmitter power). As another example,the controller 144 may dynamically generate configurations based onsignal and/or environmental conditions; the coefficients of a GMP modelare set by a formula that takes transmitter power, temperature, andreceiver power as input.

The controller 144 preferably sets transform configurations based on avariety of input data (whether transform configurations are selectedfrom a set of static configurations or generated according to a formulaor model). Input data used by the controller 144 may include staticenvironmental and system data (e.g. receiver operating characteristics,transmitter operating characteristics, receiver elevation abovesea-level), dynamic environmental and system data (e.g. current ambienttemperature, current receiver temperature, average transmitter power,ambient humidity), and/or system configuration data (e.g.receiver/transmitter settings), signal data (e.g., digital transmitsignal, RF transmit signal, RF receive signal, digital receive signal).The controller 144 may additionally or alternatively generate and/or usemodels based on this input data to set transform configurations; forexample, a transmitter manufacturer may give a model to predict internaltemperature of the transmitter based on transmitter power, and thecontroller 144 may use the output of this model (given transmitterpower) as input data for setting transform configurations.

When utilizing digital residue signals, the controller 144 preferablyutilizes an un-filtered digital residue signal (as shown in FIGS. 3A and3B), but may additionally or alternatively utilize a filtered digitalresidue signal (as shown in FIG. 3C). Likewise, any input signal dataused by the controller 144 may be in raw form, in processed form, or inany other form. The digital residue signal used by the controller may bereferred to as a controller-sampled digital residue signal.

The controller 144 may set transform configurations at any time, butpreferably sets transform configurations in response to either a timethreshold or other input data threshold being crossed. For example, thecontroller 144 may re-set transform configurations every ten secondsaccording to changed input data values. As another example, thecontroller 144 may re-set transform configurations whenever transmitterpower thresholds are crossed (e.g. whenever transmitter power increasesby ten percent since the last transform configuration setting, orwhenever transmitter power increases over some static value).

The controller 144 may cooperate with the analog canceller 160 (forinstance, setting transform configurations based on data from the analogcanceller 160, coordinating transform configuration setting times withthe analog canceller 160, disabling or modifying operation of the analogcanceller 160) to reduce overall self-interference (or for any othersuitable reason).

The controller 144 preferably adapts transform configurations and/ortransform-configuration-generating algorithms (i.e., algorithms thatdynamically generate transform configurations) to reduceself-interference for a given transmit signal and set ofsystem/environmental conditions. The controller 144 may adapt transformconfigurations and/or transform-configuration-generating algorithmsusing analytical methods, online gradient-descent methods (e.g., LMS,RLMS), and/or any other suitable methods. Adapting transformconfigurations preferably includes changing transform configurationsbased on learning. In the case of a neural-network model, this mightinclude altering the structure and/or weights of a neural network basedon test inputs. In the case of a GMP polynomial model, this mightinclude optimizing GMP polynomial coefficients according to agradient-descent method.

The controller 144 may adapt transform configurations based on testinput scenarios (e.g. scenarios when the signal received by the RFreceiver is known), scenarios where there is no input (e.g. the onlysignal received at the RF receiver is the signal transmitted by the RFtransmitter), or scenarios where the received signal is unknown. Incases where the received signal is an unknown signal, the controller 144may adapt transform configurations based on historical received data(e.g. what the signal looked like ten seconds ago) or any other suitableinformation. The controller 144 may additionally or alternatively adapttransform configurations based on the content of the transmitted signal;for instance, if the transmitted signal is modulated in a particularway, the controller 144 may look for that same modulation in theself-interference signal; more specifically, the controller 144 mayadapt transform configurations such that when the self-interferencesignal is combined with the digital receive signal the remainingmodulation (as an indicator of self-interference) is reduced (comparedto a previous transform configuration).

The controller 144 may additionally or alternatively function to settuning parameters for components outside of the digitalself-interference canceller 140, particularly if those parameters arerelevant to digital self-interference canceller performance and/ortuning.

In addition to setting the transform configuration, the controller 144may also be used to change other parameters surrounding digitalself-interference cancellation. For example, the controller 144 may beused to modify the size of the averaging window (i.e., number of samplesused to perform averaging) of a filter 142 in response to estimatedchannel characteristics.

In a first implementation of an invention embodiment, the controller 144modifies the averaging window based on channel power. For example, thecontroller 144 may modify the averaging window based on the magnitude of

$\frac{Rx}{Tx}\mspace{14mu}{or}\mspace{14mu}\frac{Rxr}{Tx}$(as estimates of self-interference channel power). Additionally oralternatively, the controller 144 may use any signal power (e.g.,magnitude of Tx) to modify the averaging window. The controller 144 mayselect averaging windows based on the value of power (e.g., higherpower->larger window), the rate of change of power

$\left( {{e.g.},{{{higher}\mspace{14mu}{\frac{d}{d\; t}\left\lbrack \frac{Rxr}{Tx} \right\rbrack}}->{{larger}\mspace{14mu}{window}}}} \right),$or in any other manner. For example, rate of change of power may befound using channel update power (e.g., the difference in estimatedchannel power between channel updates).

While the technique described in the first implementation does enablethe digital self-interference canceller 140 to adapt to changingself-interference channel conditions, it is not ideal fordifferentiating between scenarios where receive signal variation islargely due to variation in the channel and scenarios where receivesignal variation is largely due to variation in noise (i.e., Z).

In a second implementation of an invention embodiment, the controller144 modifies the averaging window based on a channel dynamics estimationη. η is preferably a metric that differentiates between the twoaforementioned scenarios (e.g., η≈0 when changes in Z are much largerthan changes in H, η≈1 when changes in H are much larger than changes inZ). η may approach a first value as the ratio of dH/dt becomes largerthan dZ/dt and a second value in the opposite case (dZ/dt becomes largerthan dH/dt). test η may differentiate between these two types of changeby looking at correlation across frequency in the power change; whiledynamic channel change is correlated across frequency, noise change isgenerally not.

This channel update change may be written as{tilde over (H)} _(i,k) =Ĥ _(i,k) −Ĥ _(i-1,k)=Δ_(i,k) +R _(i,k)where i represents time, k represents subcarrier, Δ represents thechange in channel update power due to channel dynamics, and R representsthe change in channel dynamics due to noise (e.g., an actual receivesignal).

In a first example of the second implementation, η is a correlationcoefficient written as:

$\eta = \frac{\sum\limits_{k = 1}^{K - 1}{{\overset{\sim}{H}}_{i,{k - 1}}^{*}{\overset{\sim}{H}}_{i,k}}}{\sum\limits_{k = 1}^{K - 1}{{\overset{\sim}{H}}_{i,k}}^{2}}$further noting

$\frac{\sum\limits_{k = 1}^{K - 1}{{\overset{\sim}{H}}_{i,{k - 1}}^{*}{\overset{\sim}{H}}_{i,k}}}{\sum\limits_{k = 1}^{K - 1}{{\overset{\sim}{H}}_{i,k}}^{2}} \approx \frac{{\sum\limits_{k = 1}^{K - 1}{\Delta_{i,{k - 1}}^{*}\Delta_{i,k}}} + {\sum\limits_{k = 1}^{K - 1}{R_{i,{k - 1}}^{*}R_{i,k}}}}{{K\;\sigma_{Rx}^{2}} + {\sum\limits_{k = 1}^{K - 1}{\Delta_{i,k}}^{2}}} \approx \frac{\sum\limits_{k = 1}^{K - 1}{\Delta_{i,{k - 1}}^{*}\Delta_{i,k}}}{{K\;\sigma_{Rx}^{2}} + {\sum\limits_{k = 1}^{K - 1}{\Delta_{i,k}}^{2}}}$

While this example metric operates via identification of frequencycorrelation, dynamic channel change is also correlated over time.Therefore, an alternative metric may be used:

$\eta = \frac{\sum\limits_{k = 1}^{K - 1}{{\overset{\sim}{H}}_{i,k}^{*}{\overset{\sim}{H}}_{{i + 1},k}}}{\sum\limits_{k = 1}^{K - 1}{{\overset{\sim}{H}}_{i,k}}^{2}}$Any metric measuring correlation across frequency and/or time may beused as an alternative metric for η. As another example,

$\eta^{\prime} = {\frac{{\Delta_{i,k}}^{2}}{{\Delta_{i,k}}^{2} + {R_{i,k}}^{2}} = \frac{\sum\limits_{k = 1}^{K - 1}{{A*{\overset{\sim}{H}}_{i,k}}}}{\sum\limits_{k = 1}^{K - 1}{\overset{\sim}{H}}_{i,k}}}$where A is a smoothing matrix or other filtering operation.

The controller 144 may modify averaging window in any manner based on η.For example, the controller 144 may set a smaller averaging window if ηapproaches 1 (resulting in faster updates) or a larger averaging windowif η approaches 0 (resulting in more immunity to noise).

Note that while the controller 144 preferably uses channel estimatesgenerated by the channel estimator 143 to set averaging windows, thecontroller 144 may additionally or alternatively use channel estimates(or other self-interference channel information) from any other source.For example, the controller 144 may set averaging windows based onchannel estimates received from channel memory, as shown in FIG. 6. Inthis example, the channel estimate used is less noisy; however, thisnoise may be colored due to Fourier transforms, which in turn may makethe correlation metric less reliable. As a second example, thecontroller 144 may set averaging windows based on channel estimatesreceived from the total updated channel, as shown in FIG. 7. Here, thechannel noise is white and can be easily extended to architectures wherethe entire channel is re-estimated every update (as opposed toincremental updates); however, the noise variance is higher (and thusadaptive thresholds may be needed). As a third example, the controller144 may set averaging windows based on channel estimates received fromthe time-domain (and potentially extended) total updated channel, asshown in FIG. 8. Here, the noise variance is smaller and requires fewercomputations; however, the out-of-band channel influences thecorrelation metric, making it potentially less reliable.

The channel memory 145 functions to hold data relating to pastself-interference channel estimates. In some use cases, it may bedesired that the filter 142 and channel estimator 143 operate on only asubset of a full communication channel at a time. In many communicationschemes, transmission may occur only some subchannels (of an overallfull channel) at a given time. Accordingly, it may be possible (ordesirable) only to update subchannels for which transmit signal data isavailable. In these use cases, the channel memory 145 functions to storethe last known self-interference channel for each subchannel (or anyother representation of the full self-interference channel) which may becombined with an incremental update generated by the channel estimator143 to create a new full channel estimate. As shown in FIG. 3A, this newfull channel estimate is then sent to the channel memory 145, where itmay be stored as the most recent self-interference channel estimate.

Note that as shown in FIG. 3A, the channel estimate stored by thechannel memory may be transformed before storage; however, additionallyor alternatively the channel memory 145 may be updated directly from thecombination of the channel memory 145 output and the channel estimator143 output.

The digital self-interference canceller 140 may combine channelestimates from channel memory 145 with the output of the channelestimator 143 in any manner. For example, the digital self-interferencecanceller 140 may replace a section (e.g., a sub-band) of a past channelestimate with the output of the channel estimator 143. As a secondexample, the channel estimator 143 may average the output of the channelestimator 143 and the channel memory 145 within a sub-band of relevance.

The predictor 146 functions to predict a future self-interferencecancellation channel estimate from current and/or past self-interferencecancellation channel estimates. Because performing cancellation tuningtakes time, any tuning on a varying self-interference channel isobsolete as soon as it is complete. The predictor 146 preferablymodifies the output of the channel estimator 143 to compensate for someor all of the delay incurred by the tuning process. Additionally oralternatively, the predictor 146 may predict future self-interferencecancellation channels in order to increase time in between tuning forthe canceler 140.

For example, as shown in FIG. 9A, the estimated channel lags behindactual channel. The predictor 146 may modify the estimated channel basedon past channel estimates, leading to a predicted channel, as shown inFIG. 9B. This may result in a significant reduction of error.

The predictor 146 may attempt to compensate for a known or estimatedtuning delay (e.g., the delay between sampling a signal and estimatingthe self-interference channel from that signal), but may additionally oralternatively extrapolate the self-interference channel into the futureby any amount of time (e.g., a time less than or greater than theaforementioned delay). Extrapolation may be performed usinginstantaneous time deltas, but may additionally or alternatively beperformed using filtered time deltas (e.g., averaging measured timedeltas). For example, using instantaneous time deltas, the predictor 146may perform extrapolation for a given self-interference channel estimatebased on the difference between the time at which the digital transmitsignal and residue signal were sampled and the time that a channelestimate was generated. Likewise, using filtered time deltas, thepredictor 146 may perform extrapolation based on the average of severalsuch differences (e.g., the most recent three differences).

The predictor 146 preferably predicts self-interference channel data ona per-subcarrier (or per sub-band) basis (i.e., the self-interferencechannel of each subcarrier is predicted independently). Alternatively,the predictor 146 may jointly predict time and frequency variance of theself-interference channel (discussed in later sections).

The predictor 146 preferably performs linear extrapolation of thechannel, but may additionally or alternatively perform any type ofextrapolation (e.g., quadratic). The predictor 146 may additionally oralternatively perform signal prediction using any technique (e.g.,Weiner filtering, MMSE prediction, adaptive filtering techniques such asLMS and RLS, or neural network/machine learning based techniques).

In one implementation of an invention embodiment, the predictor 146includes an offset estimator, a differentiator, and a slope estimator,as shown in FIG. 10. In this implementation, the offset estimator andslope estimator are preferably infinite impulse response (IIR) low-passfilters, but additionally or alternatively, the estimators may be anysuitable transformers capable of determining the slope and offset (i.e.,the linear characteristics) of the self-interference channel. Theestimators are preferably controlled by the controller 144 in responseto feedback received at the controller, but may additionally oralternatively be controlled in any manner. Note here that the time ΔTrepresents the amount of extrapolation desired. This concept may beextended to any order; for example a predictor 146 including a 2^(nd)derivative estimator is as shown in FIG. 11.

Further, the predictor 146 may additionally or alternatively predictself-interference channels in non-Cartesian coordinates. For example,the predictor 146 may transform self-interference channel estimates fromCartesian to polar coordinates, perform time extrapolation, and thenconvert back from polar to Cartesian coordinates.

As previously discussed, the predictor 146 preferably performsprediction on a per sub-carrier basis. In many communication schemes(e.g., LTE), even during periods of activity, not every sub-carrier isscheduled every symbol. There are two primary consequences that pertainto the predictor 146. The first is that if a sub-carrier is not active,prediction may not be useful until that sub-carrier is active again. Thesecond is that if a substantial amount of inactive time passes, theincremental self-interference channel for that sub-carrier is often notaccurately represented by an extrapolation of stale data. Further, thepresence of stale data may cause new channel estimates to convergeslowly.

The predictor 146 preferably addresses these issues by tracking theactivity of each subcarrier and managing predictor state (for eachsub-carrier) based on this activity. The predictor 146 preferablymonitors the most recent times when each sub-carrier was scheduled. Inan implementation of an invention embodiment, when a sub-carrier is (orwill be) inactive for a substantial time (i.e., a time greater than somethreshold), the prediction for that sub-carrier is disabled. This isreferred to as the RESET state. Additionally, the memory for thatprediction may be cleared (preventing stale data from influencing laterprediction). When the sub-carrier is active again, prediction is enabledin the WARMUP state. In this state, prediction begins estimating channelparameters (e.g., slope and offset), but does not yet modify the channelestimate based on these parameters. After satisfaction of the WARMUPstate (e.g., by passing a time threshold, by reaching a thresholdparameter convergence), prediction is enabled in the NOMINAL state(where prediction operates as normal). Alternatively, prediction outputand state many be managed in any way. For example, prediction may beturned on for linear components of the self-interference channel, butdisabled for higher order components of the self-interference channel.

Predicting channel estimates may have additional challenges whensub-carriers are not uniformly sampled in time. In an implementation ofan invention embodiment, the predictor 146 scales differential valuesbased on latched timestamps to compensate for non-uniform sampling, asshown in FIG. 12. Here, ΔT1 is the time at which prediction is desiredminus the current time, and ΔT2 is the current time minus the previousupdate time.

While the predictor 146 has been described in some implementations as acombination of digital elements (see e.g., FIGS. 10-12), the predictor146 may additionally or alternatively be implemented in any way; forexample, as an end-to-end transfer function performing prediction.

The predictor 146 preferably performs prediction in the frequencydomain, as shown in FIG. 13. Alternatively, the predictor 146 mayperform prediction in the time domain, as shown in FIG. 14. This canreduce the complexity of prediction (because the time domain solution isgenerally smaller than the frequency domain solution), but may alsodiminish the ability to control for prediction on a sub-carrier basis.

As another alternative configuration, the predictor 146 may occur afterthe channel estimate has been converted to time domain and then backagain to frequency domain (e.g., denoising), as shown in FIG. 15. Whiledenoising leads to a cleaner prediction, it may also increase predictioncomplexity.

In a variation of an invention embodiment, the predictor 146 mayextrapolate for a given subcarrier based not only on historical channeldata for that subcarrier, but also for neighboring subcarriers. This maybe particularly useful for communications systems where sub-carriers arescheduled intermittently.

While the predictor 146 as described above generally calculates anextrapolated channel estimate across frequencies for a given time, thepredictor 146 may additionally perform time-domain interpolation tobridge between frequency-domain extrapolations. To exemplify this,consider a purely frequency domain example of the predictor. A firstestimate of the channel is given at t₀, and a second estimate of thechannel is estimated to occur at t₁. In a first example (with notime-domain interpolation), the self-interference channel is predictedon an extrapolation of the channel estimate at t₀ extrapolated by

${{\Delta\; T} = \frac{t_{1} - t_{0}}{2}},$as shown in FIG. 16A. This channel estimate is used until the secondestimate of the channel is received (if the extrapolation is close toreality, self-interference performance will be best at t₀+ΔT, and not asgood elsewhere). In a time-domain interpolation example, theself-interference channel is predicted on an extrapolation of thechannel estimate at to extrapolated by ΔT=t₁−t₀, and then linearlyinterpolated based on the current time, as shown in FIG. 16B. Thisprovides some of the advantages of time-domain prediction without thefull complexity.

The extender 147 functions to extend self-interference channel estimatesto smooth estimate edges (e.g., if a self-interference channel iscalculated for a particular band and zero outside of that band, theremay be a discontinuity or sharp edge at the band edge). Edges or otherrapidly-varying features may require a large number of components toaccurately implement the channel estimate in the time domain (e.g., inthe interference canceller 140). Thus, it may be desirable to smoothsuch features in the frequency domain representation of theself-interference channel prior to converting the channel estimate to atime domain representation, in order to simplify the time domainrepresentation of the transform in the canceller 140.

In smoothing either the magnitude or phase response of the channelestimate in the frequency domain, it may be necessary for the extender147 to identify and/or locate edges or other similarly abrupt changesand/or rapidly varying features. Various techniques may be used tolocate the edges. A first such variation is to compute the localderivative of the response vs. frequency (e.g., using afinite-differencing scheme) at each frequency value of the response, andto consider an “edge” to be located at any frequency where the localderivative exceeds a particular threshold. Thus, a local slope that issufficiently “steep” (i.e., has a sufficiently large first derivative)can be recognized as an edge or other feature in need of smoothing. Arelated variation includes computing the local first derivative onlywithin frequency bands of the response where sharp variations are knownto occur, in order to reduce the computation time of edge detection. Inother variations, locating edges or other abrupt changes may include oneor a combination of step detection algorithms, such as 0-degree splinefitting, piecewise constant denoising, and variational methods (e.g.,the Potts model). Additionally or alternatively, abrupt changes in theresponses requiring smoothing can be located in any other suitablemanner.

As shown in FIG. 17, the extender 147 preferably includes smoothing themagnitude response of a self-interference channel estimate. In thisexample, exponential decay functions are matched to edges (i.e., wherethe derivative of magnitude response vs. frequency exceeds a threshold)of the magnitude response. However, other functions may additionally oralternatively be matched to the edges of the magnitude response, such asa polynomial function, a cubic spline, or any other suitable smoothlyvarying function. The function used is preferably selected in order tominimize the number of components needed to represent the transform inthe time domain, but can alternatively be selected for any suitablereason. The extender 147 may also extrapolate or otherwise modifymagnitude response of an estimate in any manner, including performingcurve fitting on portions of the magnitude response of an estimate. Theextender 147 may also filter portions of the magnitude response of theestimate (e.g., median filtering, convolution filtering, and/or anyother suitable smoothing filtering).

As shown in FIG. 18, the extender 147 preferably also smooths the phaseresponse of the transform. In this example, phase is extrapolatedlinearly between edges of phase response (i.e., where the derivative ofphase response vs. frequency exceeds a threshold). The extender 147 mayalso extrapolate or otherwise modify phase response of an estimate inany manner, including performing curve fitting on portions of the phaseresponse of an estimate. The extender 147 may also filter portions ofthe phase response of the estimate (e.g., median filtering, convolutionfiltering, and/or any other suitable smoothing filtering).

The digital self-interference canceller 140 may additionally oralternatively include any other components as described in U.S. patentapplication Ser. No. 15/362,289, e.g., blocker filters, filterinverters, etc. The digital self-interference canceller 140 mayadditionally or alternatively include gain/phase compensators thatfunction to modify the gain and phase of either the digital receivesignal or the digital self-interference cancellation signal such thatthe two signals are aligned in gain and phase. Gain/phase compensationthus enables the canceller 140 to compensate for gain and/or phase errorinduced by the receive chain (or other sources). Gain/phase correctionvalues are preferably set by the controller 144, but may additionally oralternatively be set in any manner.

The ADC 150 functions to convert a transmit signal from an analog signalto a digital signal; this signal is hereafter referred to as a convertedtransmit signal. Alternatively, the signal post-conversion may bereferred to as an RF-sourced digital transmit signal (assumingconversion from an RF transmit signal) or an IF-sourced digital transmitsignal (assuming conversion from an IF transmit signal). The ADC 150 ispreferably substantially similar to the ADC 111, but may additionally oralternatively be any suitable ADC.

In addition to analog-to-digital signal conversion, the ADC 150 mayperform signal scaling (in either analog or digital domains) as well asfrequency conversion (in either analog or digital domains) for inputanalog signals. In one implementation, the ADC 150 includes at least oneof a variable-gain amplifier (VGA) and a digital scaler. Thevariable-gain amplifier functions to scale an analog signal beforeconversion via the ADC 150, while the digital scaler functions to scalea digital signal after conversion via the ADC 150. Both the VGA anddigital scaler are preferably capable of scaling signals with anycomplex multiplier (e.g., resulting in both amplitude and phase shift),but may additionally or alternatively be capable of scaling signals witha subset of the set of complex numbers. For example, a VGA may only becapable of scaling signals by a real number between 1 and 4.

The ADC 151 is preferably substantially similar to the ADC 150, exceptthe ADC 151 functions to convert a receive signal from an analog signalto a digital signal. The ADC 151 preferably is used to sample a receivesignal post-self-interference cancellation (i.e., a residue signal) toevaluate self-interference canceller 140/160 performance and/or aid incanceller tuning. Note that the system 100 may include multiple ADCs151, and they may sample receive signals of the system 100 at any point.For example, the system 100 may include three ADCs 151; one coupled to areceive signal prior to any self-interference cancellation, one coupledto a receive signal after analog self-interference cancellation butprior to digital self-interference cancellation, and one coupled to thereceive signal after both analog and digital self-interferencecancellation. Likewise, one ADC 151 may couple to all three of thosesignals.

The DAC 152 functions to convert the digital self-interferencecancellation signal from a digital signal to an analog signal; thissignal is hereafter referred to as a converted digital self-interferencecancellation signal. Alternatively, the signal post-conversion may bereferred to as an digitally-sourced RF self-interference cancellationsignal (assuming conversion to RF) or a digitally-sourced IFself-interference cancellation signal (assuming conversion to IF). TheDAC 152 is preferably substantially similar to the DAC 121, but mayadditionally or alternatively be any suitable DAC.

In addition to digital-to-analog signal conversion, the DAC 152 mayperform signal scaling (in either analog or digital domains) as well asfrequency conversion (in either analog or digital domains) for inputdigital signals. In one implementation, the DAC 152 includes at leastone of a variable-gain amplifier (VGA) and a digital scaler. The digitalscaler functions to scale a digital signal before conversion via the DAC152, while the VGA functions to scale an analog signal after conversionvia the DAC 152. Both the VGA and digital scaler are preferably capableof scaling signals with any complex multiplier (e.g., resulting in bothamplitude and phase shift), but may additionally or alternatively becapable of scaling signals with a subset of the set of complex numbers.For example, a VGA may only be capable of scaling signals by a realnumber between 1 and 4.

VGAs and/or digital scalers of the ADCs 150/151 and the DAC 152 arepreferably controlled by the controller 144. For example, the controller144 could set the scale factor of a scaler (dig. scaler and/or VGA) ofthe DAC 152 based on the content and/or amplitude of a residue signal;e.g., the transform adaptor 142 may increase the gain of the DAC 152output in order to lower self-interference present in the residuesignal. As another example, the controller 144 could temporarily reducethe gain of the DAC 152 to 0 for tuning purposes (e.g., to establish abaseline level of cancellation in the residue signal, where the baselinelevel is set based solely on cancellation performed by the analogcanceller 160). As a third example, the controller 144 could increasethe gain of the ADC 151 in response to a low-amplitude residue signal(e.g., the ADC 151 VGA gain could be re-set to increase the likelihoodthat the signal is neither clipped nor lost in noise by theanalog-to-digital conversion block).

The analog self-interference canceller 160 functions to produce ananalog self-interference cancellation signal from an analog transmitsignal that can be combined with an analog receive signal to reduceself-interference present in the analog receive signal. The analogself-interference canceller 160 is preferably designed to operate at asingle frequency band, but may additionally or alternatively be designedto operate at multiple frequency bands. The analog self-interferencecanceller 160 may include any of the circuits related to analogself-interference cancellation of U.S. patent application Ser. No.14/569,354; e.g., the RF self-interference canceller, the IFself-interference canceller, associated up/downconverters, and/or tuningcircuits.

The analog self-interference canceller 160 is preferably implemented asan analog circuit that transforms an analog transmit signal into ananalog self-interference cancellation signal by combining a set offiltered, scaled, and/or delayed versions of the analog transmit signal,but may additionally or alternatively be implemented as any suitablecircuit. For instance, the analog self-interference canceller 160 mayperform a transformation involving only a single version or copy of theanalog transmit signal. The transformed signal (the analogself-interference cancellation signal) preferably represents at least apart of the self-interference component received at the receiver.

The analog self-interference canceller 160 is preferably adaptable tochanging self-interference parameters in addition to changes in theanalog transmit signal; for example, transceiver temperature, ambienttemperature, antenna configuration, humidity, and transmitter power.Adaptation of the analog self-interference canceller 160 is preferablyperformed by a tuning circuit, but may additionally or alternatively beperformed by a control circuit or other control mechanism included inthe canceller or any other suitable controller (e.g., by the controller144).

In particular, the analog self-interference canceller 160 may be paused(e.g., generation of an analog self-interference cancellation signal maytemporarily cease) or otherwise disabled by a tuning circuit or othercontroller (e.g., the controller 144). Alternatively, tuning of theanalog self-interference canceller 160 may be paused (e.g., an iterativetuning process stopped, temporarily or otherwise).

Note that while the preceding paragraphs primarily describe a SISO(single-input single-output) implementation of the system 100, thesystem 100 may additionally or alternatively be implemented as a MIMO(multiple-input, multiple-output) system (or MISO, SIMO, etc.). Thesystem 100 may be a 2×2 MIMO system, but may additionally have anysuitable number of transmit and receive signal paths. Each signal pathmay have separate antennas; alternatively, signal paths may shareantennas via a duplexer or other coupler. In one example, a 2×2 MIMOsystem has four antennas: a TX1 antenna, a TX2 antenna, an RX1 antenna,and an RX2 antenna. In another example, a 2×2 MIMO system has twoantennas: a TX1/RX1 antenna (coupled to both TX1 and RX1 signal pathsvia a duplexer) and a TX2/RX2 antenna (coupled to both TX2 and RX2signal paths via a duplexer).

Note that while a particular configuration of input/output connectionsfor the digital and analog cancellers 140 and 160 are described, anyconfiguration of these inputs and outputs (e.g., using ADCs/DACs tocouple the digital canceller to analog signals, including residuesignals, as shown in FIG. 2) may be used.

In a MIMO implementation, the transmitter 120 preferably has multipleinputs and outputs. In particular, the transmitter 120 preferablyincludes a DAC and frequency upconverter for each transmit signal path;additionally or alternatively, transmit signal paths may share DACsand/or frequency upconverters. Additionally or alternatively, thetransmitter 120 may be any suitable MIMO transmitter (or the system 100may include multiple transmitters 120); for example, the transmitter 120may include MIMO signal splitting or processing circuitry (which may beused to process a single digital signal into multiple MIMO analogsignals).

Likewise, the receiver 110 preferably has multiple inputs and outputs.In particular, the receiver 110 preferably includes an ADC and frequencydownconverter for each receive signal path; additionally oralternatively, receive signal paths may share ADCs and/or frequencydownconverters. Additionally or alternatively, the receiver 110 may beany suitable MIMO receiver (or the system 100 may include multiplereceivers 110); for example, the receiver 110 may include MIMO signalsplitting or processing circuitry (which may be used to process a singledigital signal into multiple MIMO analog signals).

In a MIMO implementation, the digital self-interference canceller 140 ispreferably designed for MIMO operating environments (i.e., multipletransmit and/or receive signals). In MIMO operating environments,self-interference may occur across separate communications streams; forexample, a TX1 signal may cause interference in both of RX1 and RX2signals. The digital self-interference canceller 140 may includemultiple cancellation sub-blocks (each incorporating some or all of thefunctionality of a SISO implementation of the digital self-interferencecanceller 140). For example, the digital self-interference canceller mayinclude sub-blocks for each possible RX/TX pairing (e.g., RX1/TX1, RX1TX2, etc.). In this implementation, each sub-block functions to removeself-interference resulting from a particular pairing; e.g., an RX1/TX2sub-block functions to remove self-interference in the RX1 receivesignal resulting from the TX2 transmit signal.

Similarly to the digital self-interference canceller 140, the analogself-interference canceller 160 (implemented in a MIMO system) may splitanalog self-interference cancellation duties into sub-blocks orsub-circuits as previously described.

The methods of the preferred embodiment and variations thereof can beembodied and/or implemented at least in part as a machine configured toreceive a computer-readable medium storing computer-readableinstructions. The instructions are preferably executed bycomputer-executable components preferably integrated with a system forwireless communication. The computer-readable medium can be stored onany suitable computer-readable media such as RAMs, ROMs, flash memory,EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or anysuitable device. The computer-executable component is preferably ageneral or application specific processor, but any suitable dedicatedhardware or hardware/firmware combination device can alternatively oradditionally execute the instructions.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. A method for digital self-interference cancellation,comprising: receiving a digital residue signal from a communicationsystem; generating a first reduced-noise digital residue signal based onthe digital residue signal; at a controller, sampling acontroller-sampled digital residue signal; at the controller,determining a digital transform configuration based on thecontroller-sampled digital residue signal; based on a sampled digitaltransmit signal and the first reduced-noise digital residue signal,generating a current self-interference channel estimate according to thedigital transform configuration; predicting a future self-interferencechannel estimate based on the current self-interference channelestimate; and generating a digital self-interference cancellation signalfrom the current self-interference channel estimate and the sampleddigital transmit signal; wherein the digital self-interferencecancellation signal is combined with a receive signal of thecommunication system to form the digital residue signal.
 2. The methodof claim 1, further comprising: before generating the reduced-noisedigital residue signal, transforming the digital residue signal from atime domain to a frequency domain; and transforming the digitalself-interference cancellation signal from the frequency domain to thetime domain.
 3. The method of claim 2, further comprising: aftertransforming the digital self-interference cancellation signal from thefrequency domain to the time domain, re-transforming the digitalself-interference cancellation signal from the time domain to thefrequency domain; after re-transforming the digital self-interferencecancellation signal from the time domain to the frequency domain, at achannel memory, storing the current self-interference channel estimate;and receiving a past self-interference channel estimate from the channelmemory; wherein the digital self-interference cancellation signal isgenerated based on the past self-interference channel estimate.
 4. Themethod of claim 3, further comprising, before transforming the digitalself-interference cancellation signal from the frequency domain to thetime domain, smoothing a self-interference channel estimate in thefrequency domain, wherein the self-interference channel estimate is oneof the current self-interference channel estimate and the futureself-interference channel estimate.
 5. The method of claim 4, whereinsmoothing the self-interference channel estimate comprises: locating anedge in the self-interference channel estimate; and modifying at leastone of a magnitude response or a phase response of the self-interferencechannel estimate at the edge.
 6. The method of claim 5, whereinmodifying at least one of the magnitude response or the phase responsecomprises modifying the magnitude response by inserting an exponentialdecay function into the magnitude response at the edge.
 7. The method ofclaim 5, wherein: smoothing the self-interference channel estimatefurther comprises locating a second edge in the self-interferencechannel estimate; and modifying at least one of the magnitude responseor the phase response comprises modifying the phase response byextrapolating the phase response from the edge to the second edge. 8.The method of claim 5, wherein locating the edge comprises: determininga sharply-varying frequency band of the self-interference channelestimate; determining a local derivative of the self-interferencechannel estimate within the sharply-varying frequency band; and locatingan edge at a frequency of the sharply-varying frequency band at whichthe local derivative exceeds a threshold value.
 9. The method of claim4, wherein predicting the future self-interference channel estimate isperformed before smoothing the self-interference channel estimate,wherein the self-interference channel estimate is the currentself-interference channel estimate.
 10. The method of claim 3, wherein:storing the current self-interference channel estimate comprises storinga plurality of subcarriers of the current self-interference channelestimate on a per-subcarrier basis; and receiving the pastself-interference channel estimate from the channel memory comprisesreceiving a plurality of subcarrier estimates of the pastself-interference channel estimate on a per-subcarrier basis.
 11. Themethod of claim 10, further comprising, during a first time interval, atthe channel memory, storing the past self-interference channel estimate,wherein: storing the current self-interference channel estimate isperformed during a second time interval after the first time interval;during the first time interval, the digital transmit signal comprises asub-signal within a first subcarrier; during the second time interval,the digital transmit signal is substantially restricted to an activesubset of subcarriers, wherein the active subset does not comprise thefirst subcarrier; receiving the plurality of subcarrier estimates of thepast self-interference channel estimate comprises receiving a firstsubcarrier estimate of the past self-interference channel estimate,wherein the first subcarrier estimate is associated with the firstsubcarrier; and generating the current self-interference channelestimate comprises: based on the digital transmit signal and thereduced-noise digital residue signal, generating an active subset ofsubcarrier estimates, associated with the active subset of subcarriers;and combining the active subset of subcarrier estimates with the firstsubcarrier estimate.
 12. The method of claim 11, wherein: the currentself-interference channel estimate consists of: the active subset ofsubcarrier estimates; and an inactive subset of subcarrier estimatesassociated with an inactive subset of subcarriers; and predicting thefuture self-interference channel estimate is not performed for theinactive subset of subcarriers.
 13. The method of claim 2, wherein:transforming the digital residue signal from the time domain to thefrequency domain is performed using a Discrete Fourier Transform (DFT)matrix; transforming the digital self-interference cancellation signalfrom the frequency domain to the time domain comprises performing anInverse Fast Fourier transform (IFFT) on the digital self-interferencecancellation signal using a reduced Inverse Discrete Fourier Transform(IDFT) matrix; and the reduced IDFT matrix is a pseudo-inverse of areduced DFT matrix, wherein the reduced DFT matrix comprises a strictsubset of elements of the DFT matrix.
 14. The method of claim 1, whereinthe controller samples the controller-sampled digital residue signalfrom at least one of the digital residue signal or the reduced-noisedigital residue signal.
 15. The method of claim 1, wherein generatingthe reduced-noise digital residue signal comprises time-averaging thedigital residue signal over an averaging window.
 16. The method of claim15, further comprising modifying the averaging window based on a rate ofchange of a ratio of the digital residue signal to the digital transmitsignal.
 17. The method of claim 15, further comprising: based on thedigital residue signal, determining a channel dynamics estimateassociated with a channel variation metric; and modifying the averagingwindow based on the channel dynamics estimate.
 18. The method of claim17, wherein modifying the averaging window comprises decreasing theaveraging window in response to an increase in the channel variationmetric.
 19. The method of claim 18, wherein: the channel dynamicsestimate is further associated with a noise variation metric; andmodifying the averaging window further comprises increasing theaveraging window in response to an increase in the noise variationmetric.
 20. The method of claim 1, further comprising: at a channelmemory, storing the current self-interference channel estimate; andreceiving a past self-interference channel estimate from the channelmemory; wherein the digital self-interference cancellation signal isgenerated based on the past self-interference channel estimate.